Job Summary

AI/ML Engineer (Mid) at Magnasoft Bengaluru Hybrid (WFO - 3 Days). Hands-on model building and the AI backend + pipelines.

Responsibilities

- Build and ship production models: object detection, segmentation, OCR/text extraction, and classification models behind our products. Not notebooks that die in a repo: models real customers depend on.
- Build the AI backend the models live in. Run the models on incoming data, then write the post-processing and pipeline logic that turns raw model output into clean, structured product data. All in Python.
- Work in the data layer. Detected and human-corrected results are stored in a document store (MongoDB). You design document structures and write the queries and aggregations your pipeline and the retraining loop depend on.
- Feed the data flywheel: the annotation correction retraining loop that makes the models better release over release.
- Own evaluation for your work: benchmarks, error analysis, and quality metrics tied to real product outcomes (cost-of-error, reviewer effort saved), not just headline accuracy.
- Deploy and run your models and your pipeline code using Docker, Kubernetes on AWS EKS, and iterate on what production tells you.
- Work under the Principal AI Engineer's technical direction, and partner with the Senior Applied ML Engineer on data quality and the eval harness.

Requirements

- 3-5 years hands-on building production ML/AI; you shipped models that real users or customers rely on, not only POCs or coursework.
- Strong Python for both model and product code. You write the backend and pipeline logic around your models - post-processing, data structures, pipeline stages, APIs - not just training scripts.
- Strong PyTorch (or TensorFlow) and solid ML fundamentals,



with the full lifecycle in your own hands: data preparation training evaluation deployment.
- MongoDB: comfortable you can design document schemas and write non-trivial aggregation queries.
- PostgreSQL working knowledge; comfortable enough to be productive, with room to deepen on the job.
- Docker and Kubernetes (we run AWS EKS), and hands-on AWS; you ship and run your own code, you don't hand it to someone else to deploy.
- Genuinely hands-on and eager to grow - you ramp fast under a strong Principal and take on more over time.
- Our stack: Python across the board; PyTorch; MongoDB; PostgreSQL; Docker/Kubernetes on AWS EKS; AWS for cloud and GPU-backed training/inference. Depth in ML and the Python backend/data layer matters most; we expect on-the-job growth on the rest.
- Robust plus: Computer Vision (detection/segmentation YOLO, Detectron2, Mask R-CNN) or OCR/document AI.
- Geospatial / GIS exposure (imagery, GDAL/geopandas, remote sensing).
- MLOps depth: MLflow, model registry, monitoring, data/label versioning.
- RAG / GenAI / agentic exposure, or data-centric ML (annotation tooling, active learning).
- Fluency with AI-assisted coding (e.g., Claude Code, Copilot, Cursor) to move faster.
- You might not be a fit if:
- You only train models and hand them off. This role writes the product/backend/pipeline code the models run inside, and works daily in the data layer.
- Your background is mostly analytics/BI/dashboards rather than building and shipping models.
- Your ML is purely academic or POC with nothing in production.

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📌 AIML Engineer (Bengaluru)
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📍 Bengaluru